VDCNN: Inspired by the results that deep convolutional networks have on image classification tasks [1]}, the authors of Very Deep Convolutional Networks for Text Classification (VDCNN) [2]} designed a similar deep neural network for the problem of classifying text. In VDCNN, many convolutions with small kernel sizes (size 3) are stacked to form a deep network, where shortcuts are present for keeping contextual information and solving the vanishing gradient problem. VDCNN employs convolutional blocks that consist of a sequence of two convolutional layers, each one followed by batch normalization and a ReLU activation.
